IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i8d10.1007_s10845-021-01794-z.html
   My bibliography  Save this article

An approach for designing smart manufacturing for the research and development of dye-sensitize solar cell

Author

Listed:
  • Jorge L. Alonso-Perez

    (Tecnológico Nacional de México - Instituto Tecnológico de Tijuana)

  • Selene L. Cardenas-Maciel

    (Tecnológico Nacional de México - Instituto Tecnológico de Tijuana)

  • Balter Trujillo-Navarrete

    (Tecnológico Nacional de México - Instituto Tecnológico de Tijuana)

  • Edgar A. Reynoso-Soto

    (Tecnológico Nacional de México - Instituto Tecnológico de Tijuana)

  • Nohe R. Cazarez-Cazarez

    (Tecnológico Nacional de México - Instituto Tecnológico de Tijuana)

Abstract

The research and development (R&D) of the scale-up process of third-generation photovoltaics (PVs) can benefit from the emerging trends and technologies related to the Industrial Internet of Things. However, to migrate the small-scale laboratory PVs products to a larger version of the industrial scale, a processing platform is needed to design, fabricate, and test the production line. In this paper, after a brief introduction of the production process of thin-film PVs, specifically dye-sensitized solar cells, the Industrial Internet Reference Architecture (IIRA) has been applied to the R&D scenario for the production of thin-film PVs, in order to synchronize and manage the large amount of data generated by the real, virtual or hybrid production devices and processes. The results of this study suggest that the future implementation of IIRA is a reliable option in a learning factory environment for multidisciplinary collaboration, research training in novel technologies and methods in the Tijuana Institute of Technology. This contribution is in order to optimize and scale-up the production process of a new generation of solar cells.

Suggested Citation

  • Jorge L. Alonso-Perez & Selene L. Cardenas-Maciel & Balter Trujillo-Navarrete & Edgar A. Reynoso-Soto & Nohe R. Cazarez-Cazarez, 2022. "An approach for designing smart manufacturing for the research and development of dye-sensitize solar cell," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2307-2320, December.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01794-z
    DOI: 10.1007/s10845-021-01794-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01794-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01794-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    2. Xifan Yao & Jiajun Zhou & Yingzi Lin & Yun Li & Hongnian Yu & Ying Liu, 2019. "Smart manufacturing based on cyber-physical systems and beyond," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2805-2817, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Benjamin Lutz & Dominik Kisskalt & Andreas Mayr & Daniel Regulin & Matteo Pantano & Jörg Franke, 2021. "In-situ identification of material batches using machine learning for machining operations," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1485-1495, June.
    2. Shuting Wang & Jie Meng & Yuanlong Xie & Liquan Jiang & Han Ding & Xinyu Shao, 2023. "Reference training system for intelligent manufacturing talent education: platform construction and curriculum development," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1125-1164, March.
    3. Emmanuel Ekene Okere & Ebrahiema Arendse & Alemayehu Ambaw Tsige & Willem Jacobus Perold & Umezuruike Linus Opara, 2022. "Pomegranate Quality Evaluation Using Non-Destructive Approaches: A Review," Agriculture, MDPI, vol. 12(12), pages 1-25, November.
    4. Xiaobao Zhu & Jing Shi & Fengjie Xie & Rouqi Song, 2020. "Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1985-2002, December.
    5. Yun Peng & Shenyi Zhao & Jizhan Liu, 2021. "Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine," Agriculture, MDPI, vol. 11(9), pages 1-16, September.
    6. Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.
    7. George Lãzãroiu & Armenia Androniceanu & Iulia Grecu & Gheorghe Grecu & Octav Neguri?ã, 2022. "Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1047-1080, December.
    8. Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
    9. Camélia Dadouchi & Bruno Agard, 2021. "Recommender systems as an agility enabler in supply chain management," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1229-1248, June.
    10. Constantin Aurelian Ionescu & Melinda Timea Fülöp & Dan Ioan Topor & Sorinel Căpușneanu & Teodora Odett Breaz & Sorina Geanina Stănescu & Mihaela Denisa Coman, 2021. "The New Era of Business Digitization through the Implementation of 5G Technology in Romania," Sustainability, MDPI, vol. 13(23), pages 1-23, December.
    11. Hien Nguyen Ngoc & Ganix Lasa & Ion Iriarte, 2022. "Human-centred design in industry 4.0: case study review and opportunities for future research," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 35-76, January.
    12. Yi Zhang & Peng Peng & Chongdang Liu & Yanyan Xu & Heming Zhang, 2022. "A sequential resampling approach for imbalanced batch process fault detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1057-1072, April.
    13. Swarit Anand Singh & K. A. Desai, 2023. "Automated surface defect detection framework using machine vision and convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1995-2011, April.
    14. Gautam Dutta & Ravinder Kumar & Rahul Sindhwani & Rajesh Kr. Singh, 2021. "Digitalization priorities of quality control processes for SMEs: a conceptual study in perspective of Industry 4.0 adoption," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1679-1698, August.
    15. Rui Liu, 2023. "An edge-based algorithm for tool wear monitoring in repetitive milling processes," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2333-2343, June.
    16. Xifan Yao & Nanfeng Ma & Jianming Zhang & Kesai Wang & Erfu Yang & Maurizio Faccio, 2024. "Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 235-255, January.
    17. Simon Micheler & Yee Mey Goh & Niels Lohse, 2021. "A transformation of human operation approach to inform system design for automation," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 201-220, January.
    18. Penglei Dai & Mahdi Hassan & Xuerong Sun & Ming Zhang & Zhengwei Bian & Dikai Liu, 2022. "A framework for multi-robot coverage analysis of large and complex structures," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1545-1560, June.
    19. Chenxi Yuan & Guoyan Li & Sagar Kamarthi & Xiaoning Jin & Mohsen Moghaddam, 2022. "Trends in intelligent manufacturing research: a keyword co-occurrence network based review," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 425-439, February.
    20. Tufail Habib & Muhammad Omair & Muhammad Salman Habib & Muhammad Zeeshan Zahir & Sikandar Bilal Khattak & Se-Jin Yook & Muhammad Aamir & Rehman Akhtar, 2023. "Modular Product Architecture for Sustainable Flexible Manufacturing in Industry 4.0: The Case of 3D Printer and Electric Toothbrush," Sustainability, MDPI, vol. 15(2), pages 1-29, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01794-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.